We use our pre-trained SEER model trunk for initialization and attach a linear classifier head on top. We full-finetune the model weights on ImageNet task for 15 epochs using SGD momentum \(0.9\) , weight decay \(1e-4\) , learning rate of \(0.04\)  for batch size 256 and finetune on 128 NVIDIA GPUs by scaling learning rate following Goyal  [1]}. We use step learning rate schedule with gamma of \(0.1\)  and decay at steps [8, 12]. After finetuning the model, we evaluate the finetuned model on all 5 datasets by performing inference only and report the top-1 accuracy of several models (including our baseline models) on all datasets including the ImageNet validation set in Table REF .
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